The present invention relates generally to the field of data processing. In particular, the present invention relates to a method for generating and using a data structure for apparel size prediction, size analysis, size grading rule development, pattern generation and data mining.
For a given manufacturer, standard sizing of its line of garments begins with the selection of body dimensions that will represent its sample size. A human fit model is recruited by specifying this combination of measurements and then choosing from among the dimensionally-qualified applicants. Often these dimensions are basic circumferences, such as bust, waist and hip, with little if any regard for other critical variables such as lengths, shoulder width, posture, breast size, seat prominence, or the relationship of front to back dimensions.
The choice of fit model is often a matter of image or other intangible factors having little if any relationship to actual prevalence of specific body configurations within the manufacturer""s target market. In fact, no current method exists for a manufacturer to determine the typical body configuration within its target market. The chosen fit model for the sample size then becomes the cornerstone for the manufacturer""s entire line of garments. All sample garments are fitted and refined on this fit model to produce the sample size pattern. The sample size is then graded to all other sizes by adding or subtracting graduated amounts to each pattern point in length and width in order to produce the patterns for each size in the garment line.
Thus the individual characteristics of the fit model""s specific body configuration are fitted and recorded in the cut of the sample size, and then reflected in every other size in the line through the grading process. Therefore, even when two different manufacturers seem to specify the same set of basic measurements, the actual fit and cut of their garments can be radically different. This is one reason individuals often exhibit brand loyalty to a specific manufacturer. Apparently the fit model chosen for their favorite brand has certain body configuration traits similar to their own. While body specifications vary among manufacturers, the major differences between them in garment cut and fit are more often in the details of its fit model""s specific body configuration.
This variability of cut and fit in standard-sized garments is not necessarily a bad thing, since it enables individuals to gravitate toward brands cut closer to their own body configuration. What remains a major issue for both consumers and manufacturers, however, is the need to understand and specify these variables. Manufacturers have a need to understand the typical body configuration of their target market through the full range of sizes in order to produce standard-sized garments that fit the greatest proportion possible of their target group in every size. Individuals have a need to understand the optimal body configuration best fit by various brands in order to find those brands cut most closely to their own body configuration.
A method for integrating clothing fit information includes several steps. The first step is to define a multi-dimensional array having a plurality of cells. Each dimension of the multi-dimensional array represents a different one of a plurality of body measurements, and each cell of the multi-dimensional array represents a different body configuration. The second step of the method is to populate the plurality of cells of the multi-dimensional array with a fit data representative of corresponding body configurations. The third step is to query the multi-dimensional array for the fit data associated with at least one cell oft he multi-dimensional array.